Top Data Engineer Jobs in Warsaw

As a Leading Data Engineer, you will manage a team delivering customer data solutions using Snowflake, ensure platform reliability and compliance, and drive engineering best practices.

Design, build and maintain Snowflake-based data warehouses and dbt models; write advanced SQL; automate pipelines with CI/CD and container concepts; use Git workflows; perform data processing with Python. Provide German-language support (minimum B2).

Design, develop, and optimize scalable data pipelines on Snowflake. Collaborate with cross-functional teams, implement data modelling and performance best practices, ensure data quality/security, automate workflows, integrate diverse data sources, and monitor/troubleshoot pipelines.

The Data Engineer will design and implement scalable data platforms, build data pipelines, collaborate with teams to ensure data quality, and streamline processes using AWS services and big data technologies.

Design, build, and maintain Azure Data Factory pipelines and ETL/ELT processes; monitor and optimize ADF; develop Python components (Azure Functions, API integrations); support Azure SQL/Synapse warehousing and occasional Power BI reporting; contribute to Azure DevOps CI/CD and collaborate with Product Owner and Architect to deliver scalable data platform improvements.

The Senior Data Engineer will design and optimize scalable data pipelines using Snowflake, ensuring data quality and mentoring junior engineers while implementing best practices.

The Data Engineer will develop, maintain, and optimize data pipelines using Apache Airflow, manage database performance, and support Elasticsearch integration. Responsibilities include building ETL scripts, handling Unix/Linux operations, and implementing CI/CD pipelines in GitLab.

Lead design, build, and maintenance of an enterprise semantic hub and knowledge graph. Integrate diverse data/metadata, define ontologies, build APIs and MCP endpoints for LLMs, optimize ingestion/query performance, and embed FAIR principles. Collaborate with data owners and AI teams to translate business needs into schemas and scalable, secure deployments in Kubernetes/GitOps environments.

Own end-to-end AI-automated data platform migration projects (1-4 concurrently): scope, plan, execute, and hand off. Act as primary customer contact, configure Datafold's Migration Agent, partner with engineering on execution, and help refine delivery playbooks.

Build and maintain scalable HR data solutions using Databricks, Spark, SQL and Python. Improve data quality, support migrations/integrations, implement validation and governance, and translate business requirements for HR, Finance, and country stakeholders.

The Data Engineer will design and maintain data pipelines, optimize performance, enforce governance, and collaborate with AI teams for innovative solutions.

Build and maintain AWS-based data pipelines and lifecycles (Glue, DMS, Redshift, S3). Develop production data transformations and CDC using Scala and Python, automate CI/CD with Bash, optimize Spark jobs (partitioning, Parquet, broadcast joins), and deliver reliable, idempotent pipelines while collaborating remotely with cross-functional teams.

Owner of marketing-focused data engineering: build and maintain integrations, tracking, and pipelines across BigQuery, ClickHouse, GCS, and external systems; troubleshoot tracking, payment, and data quality; collaborate with cross-functional teams and document data flows.

The Principal Data Engineer will improve data pipeline architecture, optimize data flows, and support data projects for various teams in the company.

Build, test, and maintain data pipelines that process AST outputs, knowledge graphs, and vector embeddings. Ingest and normalize code dependency graphs into Neo4j and vector indexes in Qdrant, transform source code into structured Markdown/JSON, monitor pipeline runs, debug errors, and collaborate with senior engineers to optimize retrieval and pipeline efficiency.

Build, test, and maintain data pipelines that process AST outputs, knowledge graphs, and vector embeddings; ingest and normalize code dependency graphs into Neo4j and Qdrant; transform source metadata into structured Markdown/JSON assets; monitor pipeline execution and resolve batch errors; collaborate with senior engineers and AI/ML engineers to optimize retrieval and pipeline efficiency.

Design, build, and maintain scalable batch and streaming data pipelines, internal tools, and APIs to support analytics, product features, and ML. Collaborate with product and engineering to improve data infrastructure, ensure reliability, testing, and observability in a cloud-native environment.

Design and implement scalable backend solutions to collect and manage large volumes of data, integrate Veeam products into BaaS offerings, develop and enhance product features, own major components, contribute to architecture, collaborate with product management to define requirements, and estimate implementation effort and capacity.

Lead Netflix’s data health analytics strategy by defining data excellence metrics, building trusted data models and dashboards, analyzing lineage and asset utilization trends, and influencing engineering investments. Partner with platform teams to capture metadata and telemetry, standardize data quality evaluations, and measure improvements in developer productivity, data friction, and tooling adoption.

Design, build, and own batch and real-time data pipelines and datasets to support analytics, experimentation, and product insights. Model and organize data for scale and fast retrieval, source from APIs and event streams, translate business requirements into engineering workstreams, collaborate with data scientists and product teams, and mentor colleagues.

Build and maintain scalable data pipelines and software to ingest, process, aggregate, visualize, and analyze system insights and productivity metrics. Collaborate with cross-functional teams to deliver distributed processing solutions, reporting, and experimentation infrastructure, prioritizing data quality and engineering excellence.

Design, build, and maintain full-stack data and AI solutions: scalable data pipelines, GenAI features (RAG, embeddings, LLM assistants), backend APIs/microservices, and frontend dashboards. Deploy and monitor components on cloud platforms, apply engineering best practices, and collaborate with stakeholders to deliver production-ready automation and analytics.

Build large-scale data pipelines and infrastructure for frontier AI model training. Develop data-processing models such as classifiers, quality filters, and labeling systems; design deduplication, quality scoring, labeling, and augmentation strategies; and create reliable tooling for researchers to explore and train on massive datasets. The role also evaluates how data quality and composition affect model outcomes, with web crawler experience as a bonus.

Build a greenfield data platform and unified Lakehouse integrating high-volume payment, marketing, and operational data across AWS and GCP. Design ingestion pipelines, create dbt medallion-layer transformations in BigQuery, and own Airflow orchestration, lineage, backfills, retries, alerting, infrastructure as code, and cost controls. The role requires autonomous ownership in a fully remote, asynchronous environment, with data mesh and Starburst Galaxy experience as preferred qualifications.

Build and ship full-stack features for a data intelligence SaaS platform, spanning backend services and frontend interfaces. Use AI coding assistants to accelerate development while applying sound engineering judgment. Participate in architecture discussions, code reviews, testing, incident response, troubleshooting, documentation, and knowledge sharing. Partner with product managers to translate requirements into technical solutions. The role emphasizes SaaS architecture, AWS cloud fundamentals, Docker, Kubernetes, CI/CD, agile delivery, autonomy, and production ownership.